Tableau-CRM-and-Einstein-Discovery-Consultant Exam Dumps - Salesforce Tableau CRM Einstein Discovery Consultant (SU24)

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Question # 17

A story created has an AUC of .62.

What two things could be done to possible improve this AUC score?

Choose 2 answers

Segment the training set.

Change the order of the story insights to descending.

Run detect sentiment on the dataflow before creating the story.

Add more rows of relevant data.

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Question # 18

What are two benefits of designing using the "Progressive Disclosure" principle? Choose 2 answers

improved ease of use for end users

Discounted EA licenses when growth is achieved

Automatic conditional formatting

Better dashboard performance

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Question # 19

How many stories can you create in Analytics Plus?

5,000

1,000, but more can be purchased

75, but more can be purchased

500, but more can be purchased

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Question # 20

A dataset for building the Einstein Discovery story contains 72 fields that are potentially relevant predictors.

Which approach is considered best practice to assess the top predictors in order to get to a meaningful and robust model?

This dataset is too big and cannot be used in Einstein Discovery. Request a new dataset with fewer predictors.

Build the story with all the predictors and indicate that Einstein Discovery should show the top predictors.

Go back to the data preparation and reduce the number of fields to less than 30 in order to produce a story.

Build a story with a first set of predictors and assess which predictors are important to the story. Then drop the less important ones and add the predictors that were omitted in the first run and assess their impact.

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Question # 21

An Einstein Analytics team wants to create a dashboard using two standard Salesforce objects. The dashboard should display data from the Case object, along with related data from the Account object. Which two processes can achieve this?

Extract data from the case and account objects using "sfdcDigest" transformation, Q use "append" transformation to add case data with account data, store as a dataset, and then build a dashboard using the generated dataset

Extract data from the case and account objects using an ETL tool, use the ETL tool to y/ Q join account and case data, and then upload the data to Einstein Analytics. Build a dashboard using the ETL generated dataset

Extract data from the case and account objects using "sfdcDigest" transformation, y/ Q use "augment" transformation to add case data with account data, store as a dataset, and then build a dashboard using the generated dataset

Extract data from the case and account objects using "dataloader", create a CSV file Q with account and case data, and then upload the CSV as a dataset to Einstein Analytics. Build a dashboard using that dataset

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